Determining the optimal sample complexity of PAC learning in the realizable setting was a central open problem in learning theory for decades. Finally, the seminal work by Hanneke (2016) gave an algorithm with a provably optimal sample complexity. His algorithm is based on a careful and structured sub-sampling of the training data and then returning a majority vote among hypotheses trained on each of the sub-samples. While being a very exciting theoretical result, it has not had much impact in practice, in part due to inefficiency, since it constructs a polynomial number of sub-samples of the training data, each of linear size. In this work, we prove the surprising result that the practical and classic heuristic bagging (a.k.a. bootstrap aggregation), due to Breimann (1996), is in fact also an optimal PAC learner. Bagging pre-dates Hanneke's algorithm by twenty years and is taught in most undergraduate machine learning courses. Moreover, we show that it only requires a logarithmic number of sub-samples to reach optimality.

经典的算法adaboost允许转换一个弱学习者，这是一种算法，它产生的假设比机会略好，成为一个强大的学习者，在获得足够的培训数据时，任意高精度。我们提出了一种新的算法，该算法从弱学习者中构建了一个强大的学习者，但比Adaboost和所有其他弱者到强大的学习者使用训练数据少，以实现相同的概括界限。样本复杂性下限表明我们的新算法使用最小可能的训练数据，因此是最佳的。因此，这项工作解决了从弱学习者中构建强大学习者的经典问题的样本复杂性。

Unlike tabular data, features in network data are interconnected within a domain-specific graph. Examples of this setting include gene expression overlaid on a protein interaction network (PPI) and user opinions in a social network. Network data is typically high-dimensional (large number of nodes) and often contains outlier snapshot instances and noise. In addition, it is often non-trivial and time-consuming to annotate instances with global labels (e.g., disease or normal). How can we jointly select discriminative subnetworks and representative instances for network data without supervision? We address these challenges within an unsupervised framework for joint subnetwork and instance selection in network data, called UISS, via a convex self-representation objective. Given an unlabeled network dataset, UISS identifies representative instances while ignoring outliers. It outperforms state-of-the-art baselines on both discriminative subnetwork selection and representative instance selection, achieving up to 10% accuracy improvement on all real-world data sets we use for evaluation. When employed for exploratory analysis in RNA-seq network samples from multiple studies it produces interpretable and informative summaries.

In the contemporary media landscape, with the vast and diverse supply of news, it is increasingly challenging to study such an enormous amount of items without a standardized framework. Although attempts have been made to organize and compare news items on the basis of news values, news genres receive little attention, especially the genres in a news consumer's perception. Yet, perceived news genres serve as an essential component in exploring how news has developed, as well as a precondition for understanding media effects. We approach this concept by conceptualizing and operationalizing a non-discrete framework for mapping news items in terms of genre cues. As a starting point, we propose a preliminary set of dimensions consisting of "factuality" and "formality". To automatically analyze a large amount of news items, we deliver two computational models for predicting news sentences in terms of the said two dimensions. Such predictions could then be used for locating news items within our framework. This proposed approach that positions news items upon a multidimensional grid helps in deepening our insight into the evolving nature of news genres.

Safety is still one of the major research challenges in reinforcement learning (RL). In this paper, we address the problem of how to avoid safety violations of RL agents during exploration in probabilistic and partially unknown environments. Our approach combines automata learning for Markov Decision Processes (MDPs) and shield synthesis in an iterative approach. Initially, the MDP representing the environment is unknown. The agent starts exploring the environment and collects traces. From the collected traces, we passively learn MDPs that abstractly represent the safety-relevant aspects of the environment. Given a learned MDP and a safety specification, we construct a shield. For each state-action pair within a learned MDP, the shield computes exact probabilities on how likely it is that executing the action results in violating the specification from the current state within the next $k$ steps. After the shield is constructed, the shield is used during runtime and blocks any actions that induce a too large risk from the agent. The shielded agent continues to explore the environment and collects new data on the environment. Iteratively, we use the collected data to learn new MDPs with higher accuracy, resulting in turn in shields able to prevent more safety violations. We implemented our approach and present a detailed case study of a Q-learning agent exploring slippery Gridworlds. In our experiments, we show that as the agent explores more and more of the environment during training, the improved learned models lead to shields that are able to prevent many safety violations.

多臂强盗（MAB）问题是一个简单而强大的框架，在不确定性下的决策背景下进行了广泛的研究。在许多实际应用程序（例如机器人应用程序）中，选择ARM对应于限制下一个可用臂（动作）选择的物理动作。在此激励的情况下，我们研究了一个称为图形匪徒的mAb的扩展，在该图形上，试图从不同节点收集的奖励来传播图形。该图定义了代理在每个步骤中选择下一个可用节点时的自由度。我们假设图形结构完全可用，但是奖励分布未知。我们建立在基于脱机图的计划算法和乐观原则的基础上，我们设计了一种在线学习算法，该算法可以使用乐观原则来平衡长期探索 - 探索。我们表明我们提出的算法达到$ o（| s | \ sqrt {t} \ log（t）+d | s | s | \ log t）$学习后悔，其中$ | s | $是节点的数量和$ d $是该图的直径，与在类似设置下的最著名的增强学习算法相比，这是优越的。数值实验证实，我们的算法优于几个基准。最后，我们提出了一个由图形匪徒框架建模的合成机器人应用程序，其中机器人在农村/郊区位置网络上移动，使用我们建议的算法提供高速Internet访问。

磁共振成像（MRI）扫描很耗时且不稳定，因为患者长时间仍在狭窄的空间中。为了减少扫描时间，一些专家已经尝试了不足采样的K空间，试图使用深度学习来预测完全采样的结果。这些研究报告说，可以节省多达20到30分钟的时间，这需要一个小时或更长时间。然而，这些研究都没有探索使用掩盖图像建模（MIM）来预测MRI K空间缺失部分的可能性。这项研究利用了11161个从Facebook的FastMRI数据集中重建的MRI和膝关节MRI图像的K空间。这使用基线移位窗口（SWIN）和视觉变压器体系结构测试了现有模型的修改版本，该窗口和视觉变压器体系结构可在未采样的K空间上使用MIM来预测完整的K空间，从而预测完整的MRI图像。使用Pytorch和Numpy库进行修改，并发布到GitHub存储库。模型重建K空间图像后，应用了基本的傅立叶变换来确定实际的MRI图像。一旦模型达到稳定状态，对超参数的实验有助于实现重建图像的精确精度。通过L1丢失，梯度归一化和结构相似性值评估了该模型。该模型产生的重建图像，L1损耗值平均为<0.01，训练完成后梯度归一化值<0.1。重建的K空间对训练和验证的结构相似性值均超过99％，并通过完全采样的K空间进行验证，而验证损失在0.01以下不断减少。这些数据强烈支持算法可用于MRI重建的想法，因为它们表明该模型的重建图像与原始的，完全采样的K空间非常吻合。

我们在运营研究和机器学习（ML）的Nexus中提出了一种方法，该方法利用了从ML提供的通用近似器，以加速混合智能线性两阶段随机程序的解决方案。我们旨在解决第二阶段高度要求的问题。我们的核心思想是通过用快速而准确的监督ML预测替换确切的第二阶段解决方案，从而在在线解决方案时间中大量减少，同时，在第一阶段解决方案准确性中略有降低。当随着时间的推移反复解决类似问题时，在与车队管理，路由和集装箱院子管理有关的运输计划中反复解决类似问题时，对ML的前期投资将是合理的。我们的数值结果集中在与整数和连续L形切口中的问题类别解决的问题类别。我们的广泛的经验分析基于从随机服务器位置（SSLP）和随机多主背包（SMKP）问题的标准化家族基础。所提出的方法可以在不到9％的时间内解决SSLP的最难实例，而在SMKP的情况下，同一图为20％。在大多数情况下，平均最佳差距少于0.1％。

关键应用程序中机器学习（ML）组件的集成引入了软件认证和验证的新挑战。正在开发新的安全标准和技术准则，以支持基于ML的系统的安全性，例如ISO 21448 SOTIF用于汽车域名，并保证机器学习用于自主系统（AMLAS）框架。 SOTIF和AMLA提供了高级指导，但对于每个特定情况，必须将细节凿出来。我们启动了一个研究项目，目的是证明开放汽车系统中ML组件的完整安全案例。本文报告说，Smikk的安全保证合作是由行业级别的行业合作的，这是一个基于ML的行人自动紧急制动示威者，在行业级模拟器中运行。我们演示了AMLA在伪装上的应用，以在简约的操作设计域中，即，我们为其基于ML的集成组件共享一个完整的安全案例。最后，我们报告了经验教训，并在开源许可下为研究界重新使用的开源许可提供了傻笑和安全案例。

The purpose of this paper is to explore the use of deep learning for the solution of the nonlinear filtering problem. This is achieved by solving the Zakai equation by a deep splitting method, previously developed for approximate solution of (stochastic) partial differential equations. This is combined with an energy-based model for the approximation of functions by a deep neural network. This results in a computationally fast filter that takes observations as input and that does not require re-training when new observations are received. The method is tested on four examples, two linear in one and twenty dimensions and two nonlinear in one dimension. The method shows promising performance when benchmarked against the Kalman filter and the bootstrap particle filter.